Sometimes we need more data pointers, research paper reads for a use case. arxiv / phd papers are a wealth of information to get right directions for dataset, features, models
Paper - Telematics and Contextual Data Analysis and Driving Risk Prediction
Key Notes
- Usecase - Driving Risk Prediction
- Solution that consists of three parts
- a) characterizing driving context,
- b) characterizing driving style, and
- c) context-aware driving risk prediction
Areas
- Driving context can be described as a combination of location (e.g., Interstate-90) and time (e.g., weekdays between 3pm to 7pm).
- Characteristics of contexts from the aggregate behavior of drivers Segmenting trajectories to identify meaningful driving patterns
- Analyze each pattern with respect to contextual data to identify cause-andeffect patterns of significance
Features
- Contextual data - traffic data
- Weather data - weather stations
- Road-network characteristics - (e.g., road type and road shape)
Analysis of telematics alongside contextual data provides valuable insights regarding an individual’s behavior, common driving habits, and characteristics of the road network with regard to dynamic traffic flow
- Roads Analysis - sharp-turn , smooth-turn, exit/merge, intersection, exit/merge, ramp, bridge
- Drive Analysis - speed, acceleration, GPS coordinates, heading
- Time features - Type of Day, Time of the Day, frequency of congestion events
- For traffic data, we have loc = (latitude, longitude, Street Name, Street Side, Zipcode, City, State)
Patterns
- Monthly Traffic Distribution
- Monthly Weather Distribution
- Weekly Traffic Distribution
Weather Entity
- Severe-Cold: the case of having extremely low temperature, with temperature
- Fog: the case where there is low visibility condition as result of fog or haze.
- Hail: the case of having solid precipitation including ice pallets and hail.
- Rain: the case of having rain, including any type of the rain, ranging from light to heavy.
- Snow: the case of having snow, including any type, ranging from light to heavy.
- Storm: the extremely windy condition, where the wind speed is at least 60kmh.
- Precipitation: a generic label which we frequently observed in raw weather data, however, we have no further information to include them in any of the previously described entity types
Road-network - Interstates and Freeways, Cities
Clustering based on Traffic patterns
Study the behavior of an individual driver in order to evaluate how risky or safe he/she is
Common propagation patterns of traffic and weather entities
rain → accident → congestion
major construction → more congestions
Tree-pattern-mining-based process, which we name short-term pattern discovery
Input: A trajectory T.
Model: A predictive model M to capture variations in driving behavior to derive driving style information
gps, accelerometer, and magnetometer
Contextual data such as traffic events, weather data, points-of-interest, and time
Comprehensive set of attributes to describe each accident including location data, time data, natural language description of event, weather data, period-of-day information22, and relevant points-of-interest data
Dataset - US Accidents
Dataset which is called the 100-car naturalistic driving study
Cluster based on accident history leveraging accident datasets
Using telematics data alone for driving risk prediction is a recent trend given notable attention in the past few years
Risk assessment for individual drivers, based on crash and near-crash
- (CNC) events, as well as critical-incident events (CIE), age, and personality of drivers to be the important risk factors
- Using K-Means clustering, they performed clustering of CNC rates, and identified three clusters of low, moderate, and high risk drivers
- Average monthly drive time, age, gender, living region, and car’s age, to predict the frequency of claims for different drivers
- Coarse-grained attributes such as yearly distance, number of trips, average Distance per trip, and coverage of different road types by distance
- Driving state variables (e.g., sharp-turn, lane-change, abnormal acceleration/deceleration, and speeding with respect to speed-limit data)
- (a) Smooth turn (b) Sharp and smooth turn (c) Sharp turn trajectories
Very Good Phd Document :)
Loved it, A lot of inspiration to apply it in use cases.
More reads
- Discovery of Driving Patterns by Trajectory Segmentation
- Artificial Intelligence and Data Science in the Automotive Industry
- Synthetic Dataset Generation of Driver Telematics
- SURVEYING OFF-BOARD AND EXTRA-VEHICULAR MONITORING AND PROGRESS TOWARDS PERVASIVE DIAGNOSTICS
- Emerging Autonomous Vehicle Risks: The Role of Telematics-Based Risk Assessment
- Predictive Maintenance for Industrial IoT of Vehicle Fleets using Hierarchical Modified Fuzzy Support Vector Machine
Happy Reading!!!
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